Distributionally Robust Co-optimization of Transmission Network Expansion Planning and Penetration Level of Renewable Generation

Transmission network expansion can significantly improve the penetration level of renewable generation.However,existing studies have not explicitly revealed and quantified the trade-off between the investment cost and penetration level of renewable generation.This paper proposes a distributionally robust optimization model to minimize the cost of transmission network expansion under uncertainty and maximize the penetration level of renewable generation.The proposed model includes distributionally robust joint chance constraints,which maximize the minimum expectation of the renewable utilization probability among a set of certain probability distributions within an ambiguity set.The proposed formulation yields a twostage robust optimization model with variable bounds of the uncertain sets,which is hard to solve.By applying the affine decision rule,second-order conic reformulation,and duality,we reformulate it into a single-stage standard robust optimization model and solve it efficiently via commercial solvers.Case studies are carried on the Garver 6-bus and IEEE 118-bus systems to illustrate the validity of the proposed method.

Transmission Network Expansion Planning Considering Uncertainties in Loads and Renewable Energy Resources

This paper proposes a novel method for transmission network expansion planning(TNEP)that take into account uncertainties in loads and renewable energy resources.The goal of TNEP is to minimize the expansion cost of candidate lines without any load curtailment.A robust linear optimization algorithm is adopted to minimize the load curtailment with uncertainties considered under feasible expansion costs.Hence,the optimal planning scheme obtained through an iterative process would be to serve loads and provide a sufficient margin for renewable energy integration.In this paper,two uncertainty budget parameters are introduced in the optimization process to limit the considered variation ranges for both the load and the renewable generation.Simulation results obtained from two test systems indicate that the uncertainty budget parameters used to describe uncertainties are essential to arrive at a compromise for the robustness and optimality,and hence,offer a range of preferences to power system planners and decision makers.

Integrated generation-transmission expansion planning for offshore oilfield power systems based on genetic Tabu hybrid algorithm

To address the planning issue of offshore oil-field power systems, an integrated generation-transmission expansion planning model is proposed. The outage cost is considered and the genetic Tabu hybrid algorithm(GTHA)is developed to find the optimal solution. With the proposed integrated model, the planning of generators and transmission lines can be worked out simultaneously,which outweighs the disadvantages of separate planning,for instance, unable to consider the influence of power grid during the planning of generation, or insufficient to plan the transmission system without enough information of generation. The integrated planning model takes into account both the outage cost and the shipping cost, which makes the model more practical for offshore oilfield power systems. The planning problem formulated based on the proposed model is a mixed integer nonlinear programming problem of very high computational complexity, which is difficult to solve by regular mathematical methods. A comprehensive optimization method based on GTHA is also developed to search the best solution efficiently.Finally, a case study on the planning of a 50-bus offshore oilfield power system is conducted, and the obtained results fully demonstrate the effectiveness of the presented model and method.

A Scenario-Based Robust Transmission Network Expansion Planning Method for Consideration of Wind Power Uncertainties

This paper uses a novel scenario generation method for tackling the uncertainties of wind power in the transmission network expansion planning(TNEP)problem.A heuristic moment matching(HMM)method is first applied to generate the typical scenarios for capturing the stochastic features of wind power,including expectation,standard deviation,skewness,kurtosis,and correlation of multiple wind farms.Then,based on the typical scenarios,a robust TNEP problem is presented and formulated.The solution of the problem is robust against all the scenarios that represent the stochastic features of wind power.Three test systems are used to verify the HMM method and is compared against Taguchi’s Orthogonal Array(OA)method.The simulation results show that the HMM method has better performance than the OA method in terms of the trade-off between robustness and economy.Additionally,the main factors influencing the planning scheme are studied,including the number of scenarios,wind farm capacity,and penalty factors,which provide a reference for system operators choosing parameters.

An approach for evaluating the impact of an intermittent renewable energy source on transmission expansion planning

We propose a new robust optimization approach to evaluate the impact of an intermittent renewable energy source on transmission expansion planning （TEP）. The objective function of TEP is composed of the investment cost of the transmission line and the operating cost of conventional generators. A method to select suitable scenarios representing the intermittent renewable energy generation and loads is proposed to obtain robust expansion planning for all possible scenarios. A meta-heuristic algorithm called adaptive tabu search （ATS） is employed in the proposed TEE ATS iterates between the main problem, which minimizes the investment and operating costs, and the subproblem, which minimizes the cost of power generation from conventional generators and curtailments of renewable energy generation and loads. The subproblem is solved by nonlinear programming （NLP） based on an interior point method. Moreover, the impact of an intermittent renewable energy source on TEP was evaluated by comparing expansion planning with and without consideration of a renewable energy source. The IEEE Reliability Test System 79 （RTS 79） was used for testing the proposed method and evaluating the impact of an intermittent renewable energy source on TEP. The results show that the proposed robust optimization approach provides a more robust solution than other methods and that the impact of an intermittent renewable energy source on TEP should bc considered.

面向高比例可再生能源的输电网规划方法研究进展与展望

为应对高比例可再生能源接入带来的消纳问题,提出了以多场景技术、鲁棒优化技术和协同规划技术为核心的输电网规划理论,涵盖不同适用条件或需求场景下的4种不同规划方法,即基于多场景的输电网随机规划方法、基于概率驱动的输电网鲁棒规划方法、考虑多源互补的网-源协同规划方法以及与配电网相协同的输电网规划方法,能够从输电网规划的角度在安全稳定前提下提高可再生能源消纳能力。在此基础上,对未来输电网规划方法进行了展望,提出了满足更高安全稳定需求、协同更广泛的源-网-荷-储柔性互动、推动大数据应用和挖掘电力市场机制激励空间的可能研究方向,并探讨了研究难点和解决方式。

计及随机模糊双重不确定性的源网荷协同规划模型

为应对电源与电网规划建设脱节导致的"弃风"等问题,文中提出一种将电源电网协同规划的模型。同时,鉴于需求侧在电力供需平衡中的潜力,将电力需求侧的能效电厂也纳入电力规划统筹考虑,提出了源网荷协同规划的概念与模型。另外,针对风电等新能源发电给电力系统带来的不确定性,从随机性和模糊性两个角度予以分析和建模,并在源网荷协同规划模型中计及双重不确定性。最后,通过IEEE 30节点系统的算例应用,表明该模型能够有效协调电源与电网的规划建设方案以及能效电厂项目的开展,实现系统总成本最小化,并可合理确定风电机组建设规模,保障资源优化利用。

考虑高比例可再生能源的交直流输电网规划挑战与展望

未来高比例可再生能源的时空分布特性将给电力系统规划带来前所未有的挑战,电力系统基本形态与运行特性将发生根本性变化。文中在调研国内外研究现状的基础上,阐述了高比例可再生能源并网的输电网规划面临的科学问题,分别从网源协同规划、输电网柔性规划、与配电网协同的输电网规划、输电规划方案综合评价与决策方法四个方面,提出了考虑高比例可再生能源并网的输电网规划的研究思路与框架。最后,对未来高比例可再生能源并网的输电网规划进行了展望与总结。

基于可靠性均衡优化的含风电电网协调规划

集群开发、远距离外送的规模化风电开发对电力系统规划提出了新的挑战,串序开展电源和电网规划往往将新能源接入的矛盾积压到电网规划阶段,使得电网规划方案难以求解甚至得不到合理方案。为此,从可靠性均衡的角度,提出一种利用虚拟机组进行源网协调规划的方法。该方法针对风电大规模接入后的电网规划,在规划电网的同时对电源规划方案进行反馈及微调,以经济性最优为目标,均衡优化源、网可靠性,确定电源及电网的可靠性优化值,从而得到与电源协调优化的电网规划方案。该方法可以有效减少电网建设投资及电力系统整体投资,并校验电源、电网规划中可靠性均衡的协调性。通过原型系统和测试系统的仿真及分析,验证了所提方法的有效性。

离散粒子群优化算法在输电网络扩展规划中的应用

输电网络扩展规划是一个非常复杂的大规模组合优化问题。文中研究了离散粒子群优化(DPSO)算法在单阶段输电网络扩展规划中的应用,提出一种基于黑板系统的多智能体协调模型。该模型中,DPSO算法利用黑板系统进行粒子间信息共享,有效地提高了算法的全局收敛能力。通过构造节点扩展矩阵和速度矩阵,建立了输电网络扩展规划问题的求解模型和DPSO求解算法。该算法已成功用于IEEE Garver-6等两个系统,计算结果表明了该方法的可行性和有效性。

大规模风电接入下输电网扩展规划的启发式优化算法

提出了大规模风电接入下的输电网有功和无功扩展规划的解决方法。计及了风电与负荷的全年时序数据,并将网络设备投资的年值成本与方案在全年所有不满足安全约束场景下采取控制措施的成本之和最小作为规划目标。为了降低求解计算的复杂性,将原问题分解为网络设备投资决策与方案的控制措施成本评估2个子问题,二者借助原问题的规划目标进行总体协调。在此基础上,通过分析规划目标与问题本身的特性,设计了启发式的优化算法,明显地提高了计算速度。算例分析验证了所提方法的正确性和求解算法的有效性。

基于离散多目标蜻蜓算法和改进FCM的风电协调输电网扩展规划研究

针对风电并网协调输电网扩展规划问题,提出一种基于离散多目标蜻蜓算法和改进FCM的风电协调输电网扩展规划方案。首先以投资建设成本、网损成本和弃风量为指标构建多目标风电协调输电网扩展规划模型。设计加权多核改进FCM算法,有效模拟风电空间分布多维运行时空特性。其次,提出柔性N-1安全校验策略,筛选出隶属度较低、出力和负荷较大的极端场景。设计离散多目标蜻蜓算法,提高算法多目标优化问题求解精度。最后,采用离散多目标蜻蜓算法对风电协调输电网扩展规划模型进行求解,提出最优规划方案确定策略。仿真实验证明了所提方法的有效性。

高比例可再生能源接入下考虑输配协同的输电网规划测试系统

以风光为主的高比例可再生能源通过不同电压等级接入电网,输电网与配电网之间的互济需求增强,促使输电网规划考虑与配电网协同,然而协同规划却面临着缺乏测试系统的难题。因此,构建了一个高比例可再生能源接入下考虑输配协同的输电网规划测试系统HRPTD-20,描述了系统的源网荷基本特性并给出了相关数据。系统包含2个区域、3个电压等级,以及3种传统和3种未来典型配电网结构场景,可再生能源装机容量占比接近55%。基于考虑输配协同的输电网规划模型求解,给出了测试系统的基准规划方案。对比分析验证了该测试系统可以用于输配电网协同规划,有助于提高可再生能源通过不同电压等级接入输配电网的消纳能力。

应对协同攻击的电力系统发输电拓展随机规划

随着能源互联网和智能电网的大力发展,中国电力系统已经发展成为一个复杂的信息物理系统,其遭受各种恶意攻击的风险大幅增加。为了保证电力系统的安全可靠运行,对各类恶意攻击进行建模并提出针对性的防御措施具有重要的现实意义。文中针对信息物理协同攻击提出了一个防御性随机规划模型,模型以攻击导致的削负荷期望量最小为目标函数,考虑多种协同攻击方案的影响,通过优化新增机组和线路达到防御攻击的目的。在建模过程中,首先在攻击人员的角度建立了协同攻击模型,并提出了协同攻击场景生成方法。然后,基于所生成的协同攻击场景提出了应对攻击的电力系统随机规划模型,并考虑了遭受攻击后运行人员以及规划人员视角下规划系统的运行情况。最后,基于改进IEEE RTS-79系统进行了算例分析,验证了所提模型的正确性和有效性。

基于广义主从分裂理论与配电网相协同的输电网规划方法

基于广义主从分裂理论提出与配电网相协同的输电网规划方法。以输电网投资成本、输电网运行成本与配电网运行成本之和最小为目标函数,考虑配电网网络约束及分布式电源,基于Karush-Kuhn-Tucker(KKT)最优性条件对输配耦合约束条件解耦,将原问题分解为输电网优化子问题与配电网优化子问题。引入计及电压幅值的直流潮流方程,使得在基于直流潮流的输电网规划模型中也能计算电压幅值,从而支撑输配协同优化计算时的变量交互。采用Benders分解混合异质分解算法的有效求解策略。以中国某省级输配电网为算例,对比传统输电网规划方法,验证了所提规划方法的有效性与优越性。

大规模风电下基于模糊场景聚类的网-储协调规划方法

受限于已有的电网水平,新建成的风电场难以将大量风电送出。从电网规划建设的角度出发,基于风电就地消纳与跨区消纳结合的思路,通过为风电接入配置储能系统、在现有电网基础上扩展线路构建风电消纳的通道,以现有风电和负荷数据为基础,利用模糊聚类方法提取运行场景的时空特性,提出了扩展规划的多目标优化模型,并利用改进粒子群优化算法进行求解。通过仿真算例从成本、网损、弃风量3个角度验证了规划方案的可行性。

考虑全寿命周期成本的发输电多阶段扩展规划

针对发输电中长期联合扩展规划,提出一种考虑全寿命周期成本的多目标、多阶段优化方法。本文建立了一个应用于系统规划的全寿命周期成本模型,并给出多设备非同步投运时的处理策略;考虑主体功能区规划对区域负荷差异化增长的影响,采用模糊聚类算法对具有时空特性的负荷、风电数据进行削减,得到扩展规划所需运行场景;以全寿命周期成本和连锁故障风险分别作为经济性和可靠性目标,以研究年限内每个水平年作为规划分阶段,建立多目标、多阶段发输电联合扩展规划模型,并利用带精英策略的快速非支配排序遗传算法进行求解。算例结果表明,利用该方法可得到帕累托解集及推荐的阶段式扩展规划序列;与单一阶段规划相比,多阶段规划在保证可靠性的同时可大幅降低投资成本。

Security value based expansion planning of power system with integration of large-scale wind power

Concerning the integration of large-scale wind power,an integrated model of generation and transmission expansion planning is proposed based on the assessment of the value of steady state and dynamic security.In the assessment of security value,the unit commitment simulation based on the predicted hourly load and wind power output data in the planning horizon is used to evaluate the costs of preventive control,emergency control and social losses due to the uncertainty of load and wind power.The cost of preventive control consists of the fuel cost of power generation,the environmental cost and the load shedding cost.This not only provides a systematic method of security assessment of power system expansion planning schemes,but also broadens the perspective of power system planning from the technology and economic assessment to the measure of the whole social value.In the assessment process,the preventive control and emergency control of cascading failures are also presented,which provides a convincing tool for cascading failure analysis of planning schemes and makes the security assessment more comprehensive and reasonable.The proposed model and method have been demonstrated by the assessment of two power system planning schemes on the New England 10-genarator 39-bus System.The importance of considering the value of security and simulating hourly system operation for the planning horizon,in expansion planning of power system with integration of large-scale wind power,has been confirmed.

Expansion Planning for Renewable Integration in Power System of Regions with Very High Solar Irradiation

In this paper,we address the long-term generation and transmission expansion planning for power systems of regions with very high solar irradiation.We target the power systems that currently rely mainly on thermal generators and that aim to adopt high shares of renewable sources.We propose a stochastic programming model with expansion alternatives including transmission lines,solar power plants(photovoltaic and concentrated solar),wind farms,energy storage,and flexible combined cycle gas turbines.The model represents the longterm uncertainty to characterize the demand growth,and the short-term uncertainty to characterize daily solar,wind,and demand patterns.We use the Saudi Arabian power system to illustrate the functioning of the proposed model for several cases with different renewable integration targets.The results show that a strong dependence on solar power for high shares of renewable sources requires high generation capacity and storage to meet the night demand.

Extreme Scenarios Based Data-adaptive Probability Uncertainty Set for Distributionally Robust Transmission Expansion Planning

Increasing penetration of renewable energy into power systems is the development trend of future energy systems.One of the main challenges is to plan the expansion scheme of transmission systems to accommodate uncertainties of wind power.In this letter,we propose a novel extreme scenarios(ESs)based data-adaptive probability uncertainty set for the transmission expansion planning problem.First,available historical data are utilized to identify data-adaptive ESs through the convex hull technology,and the probability uncertainty set with respect to the obtained ESs is then established,from which we draw the final expansion decision based on the worst-case distribution.The proposed distributionally robust transmission expansion planning(DRTEP)model can guarantee optimality of expected cost under the worst-case distribution,while ensuring feasibility of all possible wind power generation.Simulation studies are carried out on a modified IEEE RTS 24-bus system to verify the effectiveness of the proposed DRTEP model.